question_first experimentrun1_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = run1_data)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.3535248 0.07394413 -31.828422 2.618309e-222
## 2 mask_c 0.1141508 0.10022360 1.138961 2.547195e-01
## 3 feat_c 0.3238523 0.10022514 3.231248 1.232507e-03
## 4 mask_c:feat_c 0.4674695 0.20044728 2.332132 1.969375e-02
run2_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = run2_data)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.58289321 0.07710757 -33.4972722 5.281258e-246
## 2 mask_c 0.07250391 0.10332222 0.7017263 4.828499e-01
## 3 feat_c 0.35802662 0.10332944 3.4649044 5.304201e-04
## 4 mask_c:feat_c -0.19824320 0.20661826 -0.9594661 3.373240e-01
run3_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = run3_data)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.620988407 0.07867492 -33.31415457 2.408629e-243
## 2 mask_c -0.004392269 0.08480700 -0.05179135 9.586949e-01
## 3 feat_c 0.295992799 0.08481076 3.49003844 4.829511e-04
## 4 mask_c:feat_c 0.019227262 0.16967720 0.11331671 9.097795e-01
feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
family = binomial, data = question_first)
## term estimate std.error statistic p.value
## 1 (Intercept) -2.53963369 0.04792303 -52.9940169 0.000000e+00
## 2 mask_c 0.05178888 0.05473196 0.9462273 3.440327e-01
## 3 feat_c 0.32306357 0.05473604 5.9022094 3.586655e-09
## 4 mask_c:feat_c 0.09328473 0.10947429 0.8521155 3.941500e-01